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The Complete R Handbook

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

Download Course Outline

1

Introduction

  • About This All-in-One
  • 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
  • Comments
  • 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

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
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
  • A Heads-Up
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

Finding Your Center

  • 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

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