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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.
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|Section 1: Introduction and Overview of R|
Introduction to Comprehensive R Programming CoursePreview
Introduction and Getting Started
Getting Started and First R Session
First R Session (part 2)Preview
First R Session (part 3)
Matrices, Lists and Dataframes
One of the great strengths of R is the user's ability to add functions. In fact, many of the functions in Rare actually functions of functions. The structure of a function is given below.
Objects in the function are local to the function.
Functions and Default Arguments
More Examples of Functions (part 1)
More Functions Examples (part 2)
More Functions Examples (part 3)
More Functions Examples (part 4)
More Functions Examples (part 5)
More Functions Examples (part 6)
|Section 2: What are Vector Data Structures in R ?|
Homemade t-test Exercise Solution
Section 2 Exercise and Package Demonstrations
A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Nevertheless, they are often called elements.
More Examples of Vectors
Common Vector Operations and More
Findruns Example and Vectors Exercises
|Section 3: More Discussion of Vector Data Structures|
Vector-Based Programming Exercise Solution (part 1)
Vector Exercise Solution (part 2) and Begin General Vector Discussion
Continue General Vector DiscussionPreview
More General Vector Examples
More on Vectors and Vector Equality
Extended Vector Example and Exercise
|Section 4: Finish Vectors and Begin Matrices|
Finish Vector Discussion
Vector-Maker Exercise Solutions
The function matrix creates matrices.
matrix(data, nrow, ncol, byrow)
The data argument is usually a list of the elements that will fill the matrix. The nrow and ncolarguments specify the dimension of the matrix. Often only one dimension argument is needed if, for example, there are 20 elements in the data list and ncol is specified to be 4 then R will automatically calculate that there should be 5 rows and 4 columns since 4*5=20. The byrowargument specifies how the matrix is to be filled. The default value for byrow is FALSE which means that by default the matrix will be filled column by column.
seq1 <- seq(1:6)
mat1 <- matrix(seq1, 2)
mat2 <- matrix(seq1, 2, byrow = T)
Filtering Matrices and More Examples
Still More Matrices Examples
|Section 5: Finish Matrices and Begin Lists Discussion|
Min-Merge Vector Exercise Solutions
Game of Craps Exercise SolutionPreview
Naming Matrix Rows and Columns
A list is an R structure that may contain object of any other types, including other lists. Lots of the modeling functions (like t.test() for the t test or lm() for linear models) produce lists as their return values, but you can also construct one yourself:
mylist <- list (a = 1:5, b = "Hi There", c = function(x) x * sin(x))
Processing Text with ListsPreview
Applying Functions to Lists
Vector and Matrix Exercise
|Section 6: Continue Lists Discussion|
Review Programming Exercises
Finish Programming Exercise Review and Begin Discussing Lists
List Data Structures General Discussion (part 2)Preview
List Data Structures General Discussion (part 3)
Lists Data Structures General Discussion (part 4)
|Section 7: Details About Dataframe Data Structures|
A data frame is more general than a matrix, in that different columns can have different modes (numeric, character, factor, etc.). This is similar to SAS and SPSS datasets.
There are a variety of ways to identify the elements of a data frame .
A data frame is a table, or two-dimensional array-like structure, in which each column contains measurements on one variable, and each row contains one case or sample (observation) with the corresponding values for each variable for that observation.
Extracting Subdata FramesPreview
A Salary Survey Extended Example
End Dataframes Discussion; Matrix Exercise
|Section 8: More Matrix and List Examples|
Covariance Matrix Exercise Solution
An ordered collection of objects (components). A list allows you to gather a variety of (possibly unrelated) objects under one name.
Identify elements of a list using the [] convention.
List Example: Tree Growth (part 2)
Tell R that a variable is nominal by making it a factor. The factor stores the nominal values as a vector of integers in the range [ 1... k ] (where k is the number of unique values in the nominal variable), and an internal vector of character strings (the original values) mapped to these integers.
An ordered factor is used to represent an ordinal variable.
R will treat factors as nominal variables and ordered factors as ordinal variables in statistical proceedures and graphical analyses. You can use options in the factor( ) and ordered( ) functions to control the mapping of integers to strings (overiding the alphabetical ordering). You can also use factors to createvalue labels.
Factors: tapply() and split() Functions
1. Creating factor variables
Factor variables are categorical variables that can be either numeric or string variables. There are a number of advantages to converting categorical variables to factor variables. Perhaps the most important advantage is that they can be used in statistical modeling where they will be implemented correctly, i.e., they will then be assigned the correct number of degrees of freedom. Factor variables are also very useful in many different types of graphics. Furthermore, storing string variables as factor variables is a more efficient use of memory. To create a factor variable we use the factor function. The only required argument is a vector of values which can be either string or numeric. Optional arguments include the levels argument, which determines the categories of the factor variable, and the default is the sorted list of all the distinct values of the data vector. The labels argument is another optional argument which is a vector of values that will be the labels of the categories in thelevels argument.
Pascal's Triangle Exercise
|Section 9: Programming in R Environments|
Pascal's Triangle Exercise Solution
Begin Programming Structures
R Programming Environment and Scope
In order to write functions in a proper way and avoid unusual errors, we need to know the concept of environment and scope in R.R Programming Environment
Environment can be thought of as a collection of objects (functions, variables etc.). An environment is created when we first fire up the R interpreter. Any variable we define, is now in this environment. The top level environment available to us at the R command prompt is the global environment called
Nesting Multiple Environments
Referencing Variables in Other Frames
Writing to Global Variables and Recursion
As remarked at several points in this book, the purpose of the R function
inc <- function(x) return(x+1)
It instructs R to create a function that adds 1 to its argument and then assigns that function to
Sorting Programs Exercise
|Section 10: Performing Math and Simulations|
Sorting Programs Exercise Solution (part 1)
Sorting Programs Exercise Solution (part 2)
Calculating a ProbabilityPreview
Linear Algebra Operations
Performs set union, intersection, (asymmetric!) difference, equality and membership on two vectors.Usage
union(x, y) intersect(x, y) setdiff(x, y) setequal(x, y) is.element(el, set)Arguments
Combinatorial Simulations (part 1)
Combinatorial Simulations (part 2)
Winning at Roulette Exercise
|Section 11: Object Oriented Programming (OOP) and S3 and S4 Classes|
Winning at Roulette Exercise solution
Central to any object-oriented system are the concepts of class and method. A class defines the behavior of objects by describing their attributes and their relationship to other classes. The class is also used when selecting methods, functions that behave differently depending on the class of their input. Classes are usually organised in a hierarchy: if a method does not exist for a child, then the parent's method is used instead; the child inherits behaviour from the parent.
OOP Example: lm() Function
R's OO systems differ in how classes and methods are defined:
Compressing Matrices Example (part 1)
Compressing Matrices Example (part 2)
Writing S3 Classes Exercise
R's OO systems differ in how classes and methods are defined:
Implementing S4 Generic Functions
Writing S4 Classes Exercise
Live S3 and S4 Class Development
Continue S3 Class Development
Developing a Corresponding S4 Class
|Section 12: Input and Output|
Writing S3 Classes Exercise Solution
Writing S4 Classes Exercise Solution
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.