The Comprehensive Programming in R Course

Name: The Comprehensive Programming in R Course
Rating: 4.3 (268 reviews)

How to design and develop efficient general-purpose R applications for diverse tasks and domains.

Created byGeoffrey Hubona, Ph.D.

Last updated 8/2020

English

English [Auto],

What you'll 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.

Course content

14 sections • 120 lectures • 24h 59m total length

Introduction to Comprehensive R Programming Course1:52
This course teaches how to program general purpose applications in R. The first part of the course sets the stage for understanding how to program, control and manipulate basic R data structures: vectors, matrices, data frames, and lists. The second part of the course teaches the details of writing functions and programs in the object-oriented R environment: programming R structures directly; writing math and simulation functions; setting up S3 and S4 classes in R; input and output; and string manipulation and performance enhancement.
Introduction and Getting Started14:54
Explore the R studio integrated development environment and its source front end for interactive and batch R work. Learn about interpreting, vectors, R norm, help, and running code with source.
Getting Started and First R Session14:53
Set and verify the working directory in R, list files, and save a histogram to a graphical output file, while understanding how sessions and memory govern your workspace.
First R Session (part 2)14:51
Explore the R workspace with ls, objects, and search; inspect the global environment and package order; create and print vectors; explore data sets, Nile time series, and histograms.
First R Session (part 3)15:08
Learn to visualize univariate distributions in R with histograms, adjustable bins via breaks, and explore core data structures like vectors, lists, and matrices, plus text manipulation with paste and split.
Matrices, Lists and Dataframes14:58
Explore matrices, lists, and data frames in R, including indexing and row-column operations. Learn how data frames mix heterogeneous columns and how R's object oriented concepts shape analysis.
Introduction to Functions15:02
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.

myfunction <- function(arg1, arg2, ... )

{statements}

Objects in the function are local to the function.
Functions and Default Arguments14:49
Explore how R creates a fresh function environment on call, how scope separates global and local variables, and how default arguments enable lazy evaluation.
More Examples of Functions (part 1)14:17
Learn how R functions handle arguments with defaults, named versus positional calling, and lazy evaluation, including the triple dot, plus a many means example building a list.
More Functions Examples (part 2)12:00
The function accepts any vectors via triple dot, stores them in a list, and computes means and variances for each vector, illustrating scope with formal, local, and free variables.
More Functions Examples (part 3)11:12
Explore lexical scoping in R with nested functions, showing how inner frames resolve variables by climbing enclosing environments, and how super assignment pushes values to the global environment, risking pollution.
More Functions Examples (part 4)12:25
Open account creates a closure with deposit, withdraw, and balance sharing a total, showing updates and overdraft errors, and illustrating optional arguments and named versus positional binding in R.
More Functions Examples (part 5)10:19
Explore creating par functions that compare two vectors pairwise using pmax and pmin, then compute medians from resulting vectors, and use lists and named components to return multiple values.
More Functions Examples (part 6)7:31
Explore anonymous functions in R, using the function keyword to define unnamed operations and harness vectorization with x and y, while learning lazy argument evaluation in plotting.

Homemade t-test Exercise Solution15:50
Learn to implement a homemade two-sample t-test in R by building a function, computing means, variances, pooled variance, and the t-statistic step by step.
Section 2 Exercise and Package Demonstrations14:23
Explore vectors in R, using c and the colon operator to create them, learn about data modes and coercion, and see how naming elements affects indexing and matrices.
Begin Discussion of Vectors15:30
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 Vectors14:36
Explore how vectors and matrices interact in R, including element-wise addition and vector recycling. Learn on-the-fly variable creation and how mode and length influence assignments.
Common Vector Operations and More14:18
Findruns Example and Vectors Exercises14:12
learn to find runs of ones in a vector with a loop and index checks. optimize by preallocating the runs vector, then implement a sum of squares function.

Vector-Based Programming Exercise Solution (part 1)14:46
Vector Exercise Solution (part 2) and Begin General Vector Discussion16:05
Continue General Vector Discussion16:03
More General Vector Examples12:56
More on Vectors and Vector Equality16:40
Explore vectors and vector equality in R, and how operators are functions with vectorized if-else. Learn about measures of association and converting data to binary indicators.
Extended Vector Example and Exercise13:08
Explore extended vector examples and exercises in R, recoding data with nested if else and lazy evaluation, and build user defined functions that return vectors using sample.

Finish Vector Discussion16:33
Vector-Maker Exercise Solutions17:08
Develop and optimize vector making in R by building a single flexible function, measure performance with system.time, and preallocate vectors while exploring for loops, c, cat, and factorial and prod.
Begin Discussion of Matrices and Arrays14:57
Creating matrices

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 Examples15:55
Learn to filter matrices using conditions and subscripts, explore which indexes, and apply row and column operations, including means, sums, and covariance diagonals.
Still More Matrices Examples16:52
Master advanced matrix operations in R, including element-wise apply by rows or columns and transposing with t. Learn to locate the minimum value and its position in a matrix.

Min-Merge Vector Exercise Solutions15:15
Learn to find the minimum value in a vector using a for loop, and to merge two sorted vectors into a single sorted vector with a preallocated result in R.
Game of Craps Exercise Solution9:02
Demonstrates simulating craps in R by rolling two dice with a uniform distribution, first-roll win on 7 or 11, then continue until the point or a 7/11 ends.
Naming Matrix Rows and Columns15:47
Name matrix rows and columns with row names, column names, and dimension names, and use these with matrices and arrays to access and manipulate elements.
Lists: General List Operations11:48
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 Lists14:47
Discover how lists power text processing and text mining in R by parsing words, normalizing to lowercase, recording word locations, and building an inverted, named word list.
Applying Functions to Lists17:32
Explore the apply family for lists in R, comparing lapply, sapply, and apply, and learn when values are simplified and how to apply median to list components.
Vector and Matrix Exercise4:52
Create a vector maker that randomly returns numeric, character, or logical vectors with a default length of 5–10, and a matrix maker with optional rows and columns and values 1–100.

Review Programming Exercises16:08
Master lists and vectors by building a vector maker that randomly returns numeric or logical data. Build a matrix maker with optional row and column defaults and random 1–100 values.
Finish Programming Exercise Review and Begin Discussing Lists15:16
Learn to build data frames from equal-length vectors with a data frame maker, and create flexible lists using an ellipsis, exploring nested lists and single and double bracket indexing.
List Data Structures General Discussion (part 2)16:22
Explore how vectors and lists are accessed and named in R, including subsetting, named vectors, and unlisting. Learn to inspect linear model objects and extract coefficients and residuals.
List Data Structures General Discussion (part 3)15:46
Learn to store team premierships in lists, query yearly winners with a loop, and summarize data by group using the apply family and data frames in R.
Lists Data Structures General Discussion (part 4)15:48
Master lists and vectors in R and use the apply family on data frames and lists. Vectorize calculations, handle missing data, and replace loops with concise one-line expressions.

Dataframe-Maker Exercise13:52
Data Frames

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.

d <- c(1,2,3,4) e <- c("red", "white", "red", NA) f <- c(TRUE,TRUE,TRUE,FALSE) mydata <- data.frame(d,e,f) names(mydata) <- c("ID","Color","Passed") # variable names

There are a variety of ways to identify the elements of a data frame .

myframe[3:5] # columns 3,4,5 of data frame myframe[c("ID","Age")] # columns ID and Age from data frame myframe$X1 # variable x1 in the data frame
List-Maker Exercise; Begin General Dataframe Discussion15:08
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 Frames16:20
Explore subsetting data frames by rows and columns, preserve structure with drop = false, apply conditional selections with logical vectors, and handle missing data using complete.cases and new columns.
A Salary Survey Extended Example16:00
Explore working with data frames in R by using apply to compute max scores and read.csv. Filter wage data, create a ratio column, and normalize company names to lowercase.
Merging Dataframes14:29
merge data frames with the merge function to align records by matching column names, or by.x/by.y for different names; understand data frames and simple coercion with as.data.frame.
End Dataframes Discussion; Matrix Exercise14:10
Master indexing in data frames and vectors in R, then apply it to a max score exercise generating a random matrix, computing pairwise correlations, ignoring the diagonal.

Covariance Matrix Exercise Solution12:21
List Example: Tree Growth (part 1)14:05
Lists

An ordered collection of objects (components). A list allows you to gather a variety of (possibly unrelated) objects under one name.

# example of a list with 4 components - # a string, a numeric vector, a matrix, and a scaler w <- list(name="Fred", mynumbers=a, mymatrix=y, age=5.3) # example of a list containing two lists v <- c(list1,list2)

Identify elements of a list using the [[]] convention.

mylist[[2]] # 2nd component of the list mylist[["mynumbers"]] # component named mynumbers in list
List Example: Tree Growth (part 2)10:45
Learn to build a master list of tree data using apply family functions, split, and atomic operations, then plot height versus age for 65 trees in a single visualization.
Factor Data Types14:32
Factors

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.

# variable gender with 20 "male" entries and # 30 "female" entries gender <- c(rep("male",20), rep("female", 30)) gender <- factor(gender) # stores gender as 20 1s and 30 2s and associates # 1=female, 2=male internally (alphabetically) # R now treats gender as a nominal variable summary(gender)

An ordered factor is used to represent an ordinal variable.

# variable rating coded as "large", "medium", "small' rating <- ordered(rating) # recodes rating to 1,2,3 and associates # 1=large, 2=medium, 3=small internally # R now treats rating as ordinal

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() Functions15:58
Explore how tapply and split group data by factor levels, return lists, and apply functions; learn about factors, levels, and creating ordered factors with explicit level ordering.
Factor Levels versus Values10:58
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 Exercise2:37
Learn to implement a function that, given a row of Pascal's triangle, outputs the next row, exploring binomial coefficients and the triangle's properties.

Pascal's Triangle Exercise Solution10:27
Learn to build Pascal's triangle to a given depth in R by both non-recursive and recursive functions, illustrating binomial coefficients and row-by-row triangle construction.
Begin Programming Structures15:32
Master programming structures in R by examining for, while, and repeat loops, vectorized operations, and control statements like break and next, plus if else and function basics.
Environment and Scope Issues14:16
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 R_GlobalEnv. Global environment can be referred to as .GlobalEnv in R codes as well. We can use thels() function to show what variables and functions are defined in the current environment. Moreover, we can use the environment() function to get the current environment.
Nesting Multiple Environments16:06
Explore nesting of environments in R, tracing how f and h access global and parent frames. Use ls to inspect local and parent environments and understand lazy evaluation and parameters.
Referencing Variables in Other Frames14:53
Writing to Global Variables and Recursion14:05
Explore how to modify global variables in R using super assignment and assign, inspect environments, and understand recursion through a quicksort implementation with pivot logic.
Replacement and Anonymous Functions19:32
Anonymous Functions

As remarked at several points in this book, the purpose of the R function function() is to create functions. For instance, consider this code:

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 inc. However, that last step—the assignment—is not always taken. We can simply use the function object created by our call tofunction() without naming that object. The functions in that context are called anonymous, since they have no name. (That is somewhat misleading, since even nonanonymous functions only have a name in the sense that a variable is pointing to them.)
Sorting Programs Exercise7:08

Sorting Programs Exercise Solution (part 1)12:30
learn to implement and compare sorting algorithms in R, focusing on selection sort and insertion sort, and analyze efficiency by studying element comparisons during sorting.
Sorting Programs Exercise Solution (part 2)13:57
Explore bubble sort alongside selection and insertion sorts, implement two quicksort versions, and compare performance using preallocated arrays, vector swaps, and native sort benchmarks.
Calculating a Probability12:27
Compute the probability that exactly one of several independent events occurs using r, employing vectorized operations like cume prod and cume sum.
Linear Algebra Operations17:08
Apply root finding and optimization in R using Newton-Raphson for zeros and nonlinear minima. Explore matrix and vector operations, solving linear systems, and working with distributions and area under curves.
Set Operations and Simulation15:22
Set OperationsDescription

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)
Argumentsx, y, el, setvectors (of the same mode) containing a sequence of items (conceptually) with no duplicated values.Details
Each of union, intersect, setdiff and setequal will discard any duplicated values in the arguments, and they apply as.vector to their arguments (and so in particular coerce factors to character vectors).

is.element(x, y) is identical to x %in% y.
Combinatorial Simulations (part 1)10:40
Explore combinatorial simulations by estimating the expected maximum of independent normal pairs, using vectorized code, reproducible random seeds, and a practical committee-selection example.
Combinatorial Simulations (part 2)15:28
Simulate committee formation from twenty people into five, four, and three member groups to estimate the probability that persons one and two end up on the same committee, 10 percent.
Winning at Roulette Exercise7:39
Learn to simulate roulette in R by sampling ±5 with an 18/38 red probability, running 10,000 simulations of 20 bets, and analyzing cumulative winnings and a frequency distribution.

Requirements

Students will need to install the no-cost R console and the no-cost RStudio application (instructions are provided).

Description

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.

Who this course is for:

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

What you'll learn

Explore related topics

Course content

Introduction and Overview of R14 lectures • 2hr 54min

What are Vector Data Structures in R ?6 lectures • 1hr 29min

More Discussion of Vector Data Structures6 lectures • 1hr 30min

Finish Vectors and Begin Matrices5 lectures • 1hr 21min

Finish Matrices and Begin Lists Discussion7 lectures • 1hr 29min

Continue Lists Discussion5 lectures • 1hr 19min

Details About Dataframe Data Structures6 lectures • 1hr 30min

More Matrix and List Examples7 lectures • 1hr 21min

Programming in R Environments8 lectures • 1hr 52min

Performing Math and Simulations8 lectures • 1hr 45min

Requirements

Description

Who this course is for: