The Complete R Handbook

(R-BASIC.AE1) / ISBN : 978-1-64459-542-8
This course includes
Lessons
Hands-On Labs

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.

Lessons

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

Hands-On Labs

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

1

Introduction

• What You Can Safely Skip
• Icons Used in This Course
• Where to Go from Here
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
• R Structures
• for Loops and if Statements
3

Working with Packages, Importing, and Exporting

• Installing Packages
• Examining Data
• R Formulas
• More Packages
• Exploring the tidyverse
• Importing and Exporting
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
5

• 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
6

Deviating from the Average

• Measuring Variation
• Back to the Roots: Standard Deviation
• Standard Deviation in R
7

Meeting Standards and Standings

• Catching Some Zs
• Standard Scores in R
• Where Do You Stand?
• Summarizing
8

Summarizing It All

• How Many?
• The High and the Low
• Living in the Moments
• Tuning in the Frequency
• Summarizing a Data Frame
9

What’s Normal?

• Hitting the Curve
• Working with Normal Distributions
• Meeting a Distinguished Member of the Family
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
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
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
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
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
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
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
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?
18

In Due Time

• A Time Series and Its Components
• Forecasting: A Moving Experience
• Forecasting: Another Way
• Working with Real Data
19

Non-Parametric Statistics

• Independent Samples
• Matched Samples
• Correlation: Spearman’s rS
• Correlation: Kendall’s Tau
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
21

Probability Meets Regression: Logistic Regression

• Getting the Data
• Doing the Analysis
• Visualizing the Results
22

Tools and Data for Machine Learning Projects

• The UCI (University of California-Irvine) ML Repository
• Introducing the Rattle package
• Using Rattle with iris
23

Decisions, Decisions, Decisions

• Decision Tree Components
• Decision Trees in R
• Decision Trees in Rattle
• Project: A More Complex Decision Tree
• Suggested Project: Titanic
24

Into the Forest, Randomly

• Growing a Random Forest
• Random Forests in R
• Project: Identifying Glass
• Suggested Project: Identifying Mushrooms
25

Support Your Local Vector

• Some Data to Work With
• Separability: It’s Usually Nonlinear
• Support Vector Machines in R
• Project: House Parties
26

K-Means Clustering

• How It Works
• K-Means Clustering in R
• Project: Glass Clusters
27

Neural Networks

• Networks in the Nervous System
• Artificial Neural Networks
• Neural Networks in R
• Project: Banknotes
• Suggested Projects: Rattling Around
28

Exploring Marketing

• Analyzing Retail Data
• Enter Machine Learning
• Suggested Project: Another Data Set
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
30

Working with a Browser

• Getting Your Shine On
• Creating Your First shiny Project
• Working with ggplot
• Another shiny Project
• Suggested Project
31

Dashboards — How Dashing!

• The shinydashboard Package
• Exploring Dashboard Layouts
• Working with the Sidebar
• Interacting with Graphics
1

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
2

Working with Packages, Importing, and Exporting

• Analyzing Data
3

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
4

• Calculating the Mean, Median, and Mode
5

Deviating from the Average

• Finding Variance and Standard Deviation
6

Meeting Standards and Standings

• Calculating Percentiles
• Finding Nth Smallest and Nth Largest Elements
• Handling Tied Ranks
7

Summarizing It All

• Calculating Skewness and Kurtosis in Data
• Analyzing Frequency in Data
8

What’s Normal?

• Exploring Quantiles of a Normal Distribution
• Visualizing the Normal Distribution Curve
9

The Confidence Game: Estimation

• Simulating the Central Limit Theorem
• Calculating Confidence Intervals Using the T-Distribution
10

One-Sample Hypothesis Testing

• Performing the Z-Test
• Analyzing a T-Distribution
11

Two-Sample Hypothesis Testing

• Performing a Z-Test for Two Samples
• Performing a T-Test for Two Samples
• Visualizing F Distributions
12

Testing More than Two Samples

• Performing Repeated Measures ANOVA
• Performing Trend Analysis
13

More Complicated Testing

• Performing Two-Way ANOVA
• Performing Mixed ANOVA
14

Regression: Linear, Multiple, and the General Linear Model

• Creating a Linear Regression Model
• Creating a Multiple Regression Model
• Performing ANCOVA
15

Correlation: The Rise and Fall of Relationships

• Performing Correlation Analysis
• Performing Partial Correlation Analysis
16

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
17

In Due Time

• Analyzing Time Series Data
• Creating Forecasts Using Moving Averages
18

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
19

Introducing Probability

• Exploring Binomial Distribution
20

Probability Meets Regression: Logistic Regression

• Creating a Logistic Regression Model
21

Tools and Data for Machine Learning Projects

• Performing EDA
22

Decisions, Decisions, Decisions

• Creating a Decision Tree Model
23

Into the Forest, Randomly

• Creating a Random Forest Model
24

Support Your Local Vector

• Creating an SVM Model
25

K-Means Clustering

• Creating Clusters
26

Neural Networks

• Creating a Neural Network Model
27

Exploring Marketing

• Performing RFM Analysis
28

From the City That Never Sleeps

• Performing Advanced Data Analysis
29

Working with a Browser

• Analyzing Data Using the shiny App
30

Dashboards — How Dashing!

• Creating a shiny Dashboard