Extra Fundamentals of R

Extra Fundamentals of R, available at $39.99, has an average rating of 4.3, with 66 lectures, based on 22 reviews, and has 1517 subscribers.

You will learn about Understand and use the base, lattice and ggplot graphics systems in R. Be able to simulate many real-world and practical what-if scenarios to determine likely outcomes. Have a though understanding of, and ability to effectively use, the text and string variable processing capabilities in R. Know how to use and implement Rs text-based regular expression features and functions. This course is ideal for individuals who are Any novice or intermediate R user would benefit from this course. or Appropriate candidate students for this course include undergraduate and graduate students, college and university faculty, and practicing professionals, particularly in quantitative or analytics fields. or It is useful to have some rudimentary exposure to using R in a sample session, executing R script. It is particularly useful for Any novice or intermediate R user would benefit from this course. or Appropriate candidate students for this course include undergraduate and graduate students, college and university faculty, and practicing professionals, particularly in quantitative or analytics fields. or It is useful to have some rudimentary exposure to using R in a sample session, executing R script.

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Summary

Title: Extra Fundamentals of R

Price: $39.99

Average Rating: 4.3

Number of Lectures: 66

Number of Published Lectures: 66

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 66

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Understand and use the base, lattice and ggplot graphics systems in R.

Be able to simulate many real-world and practical what-if scenarios to determine likely outcomes.

Have a though understanding of, and ability to effectively use, the text and string variable processing capabilities in R.

Know how to use and implement Rs text-based regular expression features and functions.

Who Should Attend

Any novice or intermediate R user would benefit from this course.

Appropriate candidate students for this course include undergraduate and graduate students, college and university faculty, and practicing professionals, particularly in quantitative or analytics fields.

It is useful to have some rudimentary exposure to using R in a sample session, executing R script.

Target Audiences

Any novice or intermediate R user would benefit from this course.

Appropriate candidate students for this course include undergraduate and graduate students, college and university faculty, and practicing professionals, particularly in quantitative or analytics fields.

It is useful to have some rudimentary exposure to using R in a sample session, executing R script.

Extra Fundamentals of R is an extension of the Udemy course Essential Fundamentals of R. Extra Fundamentals of R introduces additional topics of interest and relevance utilizing many specific R-scripted examples. These broad topics include:

(1) Details on using Base, GGPlot and Lattice graphics;

(2) An introduction to programming and simulation in R; and

(3) Character and string processing in R.

All materials, scripts, slides, documentation and anything used or viewed in any one of the video lessons is provided with the course. The course is useful for both R-novices, as well as to intermediate R users. Rather than focus on specific and narrow R-supported skill sets, the course paints a broad canvas illustrating many specific examples in three domains that any R user would find useful. The course is a natural extension of the more basic Udemy course, Essential Fundamentals of R and is highly recommended for those students, as well as for other new students (and for practicing professionals) interested in the three domains enumerated above.

Base, GGplot and Lattice (or trellis) graphics are the three principal graphics systems in R. They each operate under different rules and each present useful and often brilliant graphics displays. However, each of these three graphics systems are generally designed and used for different domains or applications.

There are many different programming and simulation scenarios that can be modeled with R. This course provides a good sense for some of the potential simulation applications through the presentation of down-to-earth, practical domains or tasks that are supported. The examples are based on common and interesting real-world tasks: (1) simulating a game of coin-tossing; (2) returning Top-Hats checked into a restaurant to their rightful owners; (3) collecting baseball cards and state quarters for profit: (4) validating whether so-called streaky behavior, such as have a string of good-hitting behavior in consecutive baseball games, is really unusual from a statistical point of view; (4) estimating the number of taxicabs in a newly-visited city; and (5) estimating arrival times for Sam and Annie at the Empire State Building (Sleepless in Seattle).

R is likely best known for the ability to process numerical data, but R also has quite extensive capabilities to process non-quantitative text (or character) and string variables. R also has very good facilities for implementing powerful regular expression natural-language functions. An R user is bested served with an understanding of how these text (or character) and string processing capabilities work.

Most sessions present hands-on material that make use of many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is utilized in the program, supplemented with R-based direct scripts (e.g. command-line prompts) when necessary.

Course Curriculum

Chapter 1: Base and GGPlot2 Graphics in R

Lecture 1: Introduction

Lecture 2: Comparing Base and GGPlot2 Graphics

Lecture 3: Continue Graphics Capabilities and Comparisons

Lecture 4: More Graphics Capabilities and Comparisons

Lecture 5: Adding Text to Graphics

Lecture 6: Mathematical and Drawing Functions

Chapter 2: Finish Base Graphics Capabilities, Begin Lattice Graphics

Lecture 1: Fitting Non-Linear Curves in Base

Lecture 2: More Base Non-Linear Plots

Lecture 3: Base Boxplots and Bargraphs

Lecture 4: Introduction to Lattice Graphics

Lecture 5: Superposition and Lattice Exercise

Lecture 6: Lattice Exercise Solution

Chapter 3: Lattice and GGPlot Graphics

Lecture 1: In Living Color Exercises Solution Explained

Lecture 2: Finish In Living Color Exercise and Begin Lattice Graphics

Lecture 3: Lattice Layouts, Groups, and Aspect Ratios

Lecture 4: Plotting the Titanic Data Set and Begin GGPlot Graphics

Lecture 5: GGPlot: Non-Linear Fits and Plots

Lecture 6: Histograms, Bar Charts and Density Plots

Chapter 4: Programming and Simulation 1

Lecture 1: Cuckoohost and Other Plots

Lecture 2: Finish Cuckoohosts and Begin Simulation

Lecture 3: Simulating a Coin Tossing Game of Chance (part 1)

Lecture 4: Simulating a Coin Tossing Game of Chance (part 2)

Lecture 5: Simulating the Return of Top-Hats to Rightful Owners (part 1)

Lecture 6: Finish Simulating Top-Hat Returns and Begin Collecting Baseball Cards for Profit

Chapter 5: Programming and Simulation 2

Lecture 1: Collecting Baseball Cards (part 1 continued)

Lecture 2: Collecting Baseball Cards (part 2)

Lecture 3: Collecting Baseball Cards (part 3)

Lecture 4: Collecting Quarters Exercise Solution

Lecture 5: Streaky Baseball Behavior (part 1)

Lecture 6: Streaky Baseball Behavior (part 2)

Lecture 7: Sam and Annie Arrive at the Empire State Building (part 1)

Lecture 8: Hats and Streakiness Exercise

Lecture 9: Sam and Annie Arrive at the Empire State Building (part 2)

Chapter 6: Programming and Simulation 3

Lecture 1: Checking Hats Exercise Solution

Lecture 2: More Streakiness Exercise Solution

Lecture 3: Standard Normal Monte Carlo Simulation

Lecture 4: Estimating Mean Squared Error of a Trimmed Mean

Lecture 5: Estimating a Confidence Level

Lecture 6: Empirical Confidence Level

Lecture 7: Estimating the Taxi Population (part 1)

Lecture 8: Estimating the Taxi Population (part 2)

Lecture 9: Permutation Tests (part 1)

Lecture 10: Permutation Tests (part 2)

Lecture 11: The Bootstrap and Jackknife (part 1)

Lecture 12: The Bootstrap and Jackknife (part 2)

Lecture 13: Late to Class Again Exercise

Chapter 7: Character Manipulation and String Processing

Lecture 1: Late to Class Exercise Solution

Lecture 2: Character and String Manipulation

Lecture 3: Displaying and Concatenating Strings (part 1)

Lecture 4: Displaying and Concatenating Strings (part2)

Lecture 5: Manipulating Parts of a String

Lecture 6: Breaking Apart Character Values

Lecture 7: What are Regular Expressions? (slides)

Lecture 8: Using Regular Expressions in R (part 1)

Lecture 9: Using Regular Expressions in R (part 2)

Lecture 10: Reversing a String Exercise

Chapter 8: More Text and String Processing

Lecture 1: Reverse String Exercise Solution

Lecture 2: The Regexpr() and Gregexpr() Functions (part 1)

Lecture 3: The Regexpr() and Gregexpr() Functions (part 2)

Lecture 4: Testing a Filename for a Suffix

Lecture 5: Forming Filenames

Lecture 6: Substituting Text and Tagging Text

Lecture 7: Finding Words in Text Passages

Lecture 8: Manipulating the Component Names of List Structures

Lecture 9: Sorting and Ordering Words

Lecture 10: Determining and Plotting Word Frequency

Instructors

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

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5 stars: 10 votes

Frequently Asked Questions

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