The Complete R Handbook


This course includes
Hands-On Labs
AI Tutor (Add-on)

The Complete R Handbook course is designed to equip you with the skills and knowledge needed to leverage R for statistical analysis, data manipulation, visualization, and more. The course helps you dive into the basics of R programming, including data types, variables, functions, and control structures and Learn how to manipulate data in R using packages like dplyr and tidyr for efficient data wrangling. The course helps you explore statistical analysis techniques in R, including hypothesis testing, regression analysis, and ANOVA.


31+ Lessons | 34+ Exercises | 174+ Quizzes | 109+ Flashcards | 109+ Glossary of terms

Hands-On Labs

57+ LiveLab | 57+ Video tutorials | 02:39+ Hours

Here's what you will learn

Download Course Outline

Lessons 1: Introduction

  • About This All-in-One
  • What You Can Safely Skip
  • Icons Used in This Course
  • Where to Go from Here

Lessons 2: R: What It Does and How It Does It

  • The Statistical (and Related) Ideas You Just Have to Know
  • Getting R
  • Getting RStudio
  • A Session with R
  • R Functions
  • User-Defined Functions
  • Comments
  • R Structures
  • for Loops and if Statements

Lessons 3: Working with Packages, Importing, and Exporting

  • Installing Packages
  • Examining Data
  • R Formulas
  • More Packages
  • Exploring the tidyverse
  • Importing and Exporting

Lessons 4: Getting Graphic

  • Finding Patterns
  • Doing the Basics: Base R Graphics, That Is
  • Kicking It Up a Notch to ggplot2
  • Putting a Bow On It

Lessons 5: Finding Your Center

  • Means: The Lure of Averages
  • Calculating the Mean
  • The Average in R: mean()
  • Medians: Caught in the Middle
  • The Median in R: median()
  • Statistics à la Mode
  • The Mode in R

Lessons 6: Deviating from the Average

  • Measuring Variation
  • Back to the Roots: Standard Deviation
  • Standard Deviation in R

Lessons 7: Meeting Standards and Standings

  • Catching Some Zs
  • Standard Scores in R
  • Where Do You Stand?
  • Summarizing

Lessons 8: Summarizing It All

  • How Many?
  • The High and the Low
  • Living in the Moments
  • Tuning in the Frequency
  • Summarizing a Data Frame

Lessons 9: What’s Normal?

  • Hitting the Curve
  • Working with Normal Distributions
  • Meeting a Distinguished Member of the Family

Lessons 10: The Confidence Game: Estimation

  • Understanding Sampling Distributions
  • An EXTREMELY Important Idea: The Central Limit Theorem
  • Confidence: It Has Its Limits!
  • Fit to a t

Lessons 11: One-Sample Hypothesis Testing

  • Hypotheses, Tests, and Errors
  • Hypothesis Tests and Sampling Distributions
  • Catching Some Z’s Again
  • Z Testing in R
  • t for One
  • t Testing in R
  • Working with t-Distributions
  • Visualizing t-Distributions
  • Testing a Variance
  • Working with Chi-Square Distributions
  • Visualizing Chi-Square Distributions

Lessons 12: Two-Sample Hypothesis Testing

  • Hypotheses Built for Two
  • Sampling Distributions Revisited
  • t for Two
  • Like Peas in a Pod: Equal Variances
  • t-Testing in R
  • A Matched Set: Hypothesis Testing for Paired Samples
  • Paired Sample t-testing in R
  • Testing Two Variances
  • Working with F Distributions
  • Visualizing F Distributions

Lessons 13: Testing More than Two Samples

  • Testing More than Two
  • ANOVA in R
  • Another Kind of Hypothesis, Another Kind of Test
  • Getting Trendy
  • Trend Analysis in R

Lessons 14: More Complicated Testing

  • Cracking the Combinations
  • Two-Way ANOVA in R
  • Two Kinds of Variables … at Once
  • After the Analysis
  • Multivariate Analysis of Variance

Lessons 15: Regression: Linear, Multiple, and the General Linear Model

  • The Plot of Scatter
  • Graphing Lines
  • Regression: What a Line!
  • Linear Regression in R
  • Juggling Many Relationships at Once: Multiple Regression
  • ANOVA: Another Look
  • Analysis of Covariance: The Final Component of the GLM
  • But Wait — There’s More

Lessons 16: Correlation: The Rise and Fall of Relationships

  • Understanding Correlation
  • Correlation and Regression
  • Testing Hypotheses about Correlation
  • Correlation in R
  • Multiple Correlation
  • Partial Correlation
  • Partial Correlation in R
  • Semipartial Correlation
  • Semipartial Correlation in R

Lessons 17: Curvilinear Regression: When Relationships Get Complicated

  • What Is a Logarithm?
  • What Is e?
  • Power Regression
  • Exponential Regression
  • Logarithmic Regression
  • Polynomial Regression: A Higher Power
  • Which Model Should You Use?

Lessons 18: In Due Time

  • A Time Series and Its Components
  • Forecasting: A Moving Experience
  • Forecasting: Another Way
  • Working with Real Data

Lessons 19: Non-Parametric Statistics

  • Independent Samples
  • Matched Samples
  • Correlation: Spearman’s rS
  • Correlation: Kendall’s Tau
  • A Heads-Up

Lessons 20: Introducing Probability

  • What Is Probability?
  • Compound Events
  • Conditional Probability
  • Large Sample Spaces
  • R Functions for Counting Rules
  • Random Variables: Discrete and Continuous
  • Probability Distributions and Density Functions
  • The Binomial Distribution
  • The Binomial and Negative Binomial in R
  • Hypothesis Testing with the Binomial Distribution
  • More on Hypothesis Testing: R versus Tradition

Lessons 21: Probability Meets Regression: Logistic Regression

  • Getting the Data
  • Doing the Analysis
  • Visualizing the Results

Lessons 22: Tools and Data for Machine Learning Projects

  • The UCI (University of California-Irvine) ML Repository
  • Introducing the Rattle package
  • Using Rattle with iris

Lessons 23: Decisions, Decisions, Decisions

  • Decision Tree Components
  • Decision Trees in R
  • Decision Trees in Rattle
  • Project: A More Complex Decision Tree
  • Suggested Project: Titanic

Lessons 24: Into the Forest, Randomly

  • Growing a Random Forest
  • Random Forests in R
  • Project: Identifying Glass
  • Suggested Project: Identifying Mushrooms

Lessons 25: Support Your Local Vector

  • Some Data to Work With
  • Separability: It’s Usually Nonlinear
  • Support Vector Machines in R
  • Project: House Parties

Lessons 26: K-Means Clustering

  • How It Works
  • K-Means Clustering in R
  • Project: Glass Clusters

Lessons 27: Neural Networks

  • Networks in the Nervous System
  • Artificial Neural Networks
  • Neural Networks in R
  • Project: Banknotes
  • Suggested Projects: Rattling Around

Lessons 28: Exploring Marketing

  • Analyzing Retail Data
  • Enter Machine Learning
  • Suggested Project: Another Data Set

Lessons 29: From the City That Never Sleeps

  • Examining the Data Set
  • Warming Up
  • Quick Suggested Project: Airline Names
  • Suggested Project: Departure Delays
  • Quick Suggested Project: Analyze Weekday Differences
  • Suggested Project: Delay and Weather

Lessons 30: Working with a Browser

  • Getting Your Shine On
  • Creating Your First shiny Project
  • Working with ggplot
  • Another shiny Project
  • Suggested Project

Lessons 31: Dashboards — How Dashing!

  • The shinydashboard Package
  • Exploring Dashboard Layouts
  • Working with the Sidebar
  • Interacting with Graphics

Hands-on LAB Activities

R: What It Does and How It Does It

  • Performing Basic Operations
  • Creating and Using Custom Functions
  • Creating and Working with Data Frames
  • Working with Matrices
  • Using for Loops and if-else Statements

Working with Packages, Importing, and Exporting

  • Analyzing Data

Getting Graphic

  • Creating a Scatter Plot and a Box Plot
  • Creating a Bar Plot and a Pie Graph
  • Creating a Histogram and a Density Plot
  • Creating a Grouped Bar Plot with ggplot2

Finding Your Center

  • Calculating the Mean, Median, and Mode

Deviating from the Average

  • Finding Variance and Standard Deviation

Meeting Standards and Standings

  • Calculating Percentiles
  • Finding Nth Smallest and Nth Largest Elements
  • Handling Tied Ranks

Summarizing It All

  • Calculating Skewness and Kurtosis in Data
  • Analyzing Frequency in Data

What’s Normal?

  • Exploring Quantiles of a Normal Distribution
  • Visualizing the Normal Distribution Curve

The Confidence Game: Estimation

  • Simulating the Central Limit Theorem
  • Calculating Confidence Intervals Using the T-Distribution

One-Sample Hypothesis Testing

  • Performing the Z-Test
  • Analyzing a T-Distribution

Two-Sample Hypothesis Testing

  • Performing a Z-Test for Two Samples
  • Performing a T-Test for Two Samples
  • Visualizing F Distributions

Testing More than Two Samples

  • Performing Repeated Measures ANOVA
  • Performing Trend Analysis

More Complicated Testing

  • Performing Two-Way ANOVA
  • Performing Mixed ANOVA

Regression: Linear, Multiple, and the General Linear Model

  • Creating a Linear Regression Model
  • Creating a Multiple Regression Model
  • Performing ANCOVA

Correlation: The Rise and Fall of Relationships

  • Performing Correlation Analysis
  • Performing Partial Correlation Analysis

Curvilinear Regression: When Relationships Get Complicated

  • Creating a Power Regression Model
  • Creating an Exponential Regression Model
  • Creating a Logarithmic Regression Model
  • Creating a Polynomial Regression Model

In Due Time

  • Analyzing Time Series Data
  • Creating Forecasts Using Moving Averages

Non-Parametric Statistics

  • Performing the Kruskal-Wallis Rank-Sum Test
  • Performing the Wilcoxon Rank-Sum Test
  • Performing the Cochran’s Q Test
  • Performing the Friedman Rank-Sum Test

Introducing Probability

  • Exploring Binomial Distribution

Probability Meets Regression: Logistic Regression

  • Creating a Logistic Regression Model

Tools and Data for Machine Learning Projects

  • Performing EDA

Decisions, Decisions, Decisions

  • Creating a Decision Tree Model

Into the Forest, Randomly

  • Creating a Random Forest Model

Support Your Local Vector

  • Creating an SVM Model

K-Means Clustering

  • Creating Clusters

Neural Networks

  • Creating a Neural Network Model

Exploring Marketing

  • Performing RFM Analysis

From the City That Never Sleeps

  • Performing Advanced Data Analysis

Working with a Browser

  • Analyzing Data Using the shiny App

Dashboards — How Dashing!

  • Creating a shiny Dashboard