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
Interactive Lessons
31+ Interactive 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