The Complete R Handbook
(R-BASIC.AE1)/ISBN:978-1-64459-542-8
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
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Here's what you will learn
Download Course OutlineLessons 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
- Working with Matrices
- Creating and Working with Data Frames
- Using for Loops and if-else Statements
Working with Packages, Importing, and Exporting
- Analyzing Data
Getting Graphic
- Creating a Histogram and a Density Plot
- Creating a Bar Plot and a Pie Graph
- Creating a Scatter Plot and a Box 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
- Handling Tied Ranks
- Finding Nth Smallest and Nth Largest Elements
- Calculating Percentiles
Summarizing It All
- Calculating Skewness and Kurtosis in Data
- Analyzing Frequency in Data
What’s Normal?
- Visualizing the Normal Distribution Curve
- Exploring Quantiles of a Normal Distribution
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 Wilcoxon Rank-Sum Test
- Performing the Kruskal-Wallis Rank-Sum Test
- Performing the Friedman Rank-Sum Test
- Performing the Cochran’s Q 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