# The Complete R Handbook

(R-BASIC.AE1) / ISBN : 978-1-64459-542-8

## About This Course

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.

### Skills You’ll Get

## Get the support you need. Enroll in our Instructor-Led Course.

### Lessons

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

### Hands-On Labs

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

### Introduction

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

### 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

### Working with Packages, Importing, and Exporting

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

### Getting Graphic

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

### 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

### Deviating from the Average

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

### Meeting Standards and Standings

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

### Summarizing It All

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

### What’s Normal?

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

### The Confidence Game: Estimation

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

### 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

### 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

### 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

### More Complicated Testing

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

### 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

### 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

### 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?

### In Due Time

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

### Non-Parametric Statistics

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

### 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

### Probability Meets Regression: Logistic Regression

- Getting the Data
- Doing the Analysis
- Visualizing the Results

### Tools and Data for Machine Learning Projects

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

### Decisions, Decisions, Decisions

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

### Into the Forest, Randomly

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

### Support Your Local Vector

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

### K-Means Clustering

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

### Neural Networks

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

### Exploring Marketing

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

### 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

### Working with a Browser

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

### Dashboards — How Dashing!

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

### 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