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Home
All Programs
Computers
Data Science
Data Modeling & Design
R All-in-One
R All-in-One
Curriculum
36 Sections
171 Lessons
10 Weeks
Expand all sections
Collapse all sections
Introduction
6
1.1
About This All-in-One
1.2
What You Can Safely Skip
1.3
Foolish Assumptions
1.4
Icons Used in This Book
1.5
Beyond This Book
1.6
Where to Go from Here
Book 1: Introducing R
0
Chapter 1: R: What It Does and How It Does It
9
3.1
The Statistical (and Related) Ideas You Just Have to Know
3.2
Getting R
3.3
Getting RStudio
3.4
A Session with R
3.5
R Functions
3.6
User-Defined Functions
3.7
Comments
3.8
R Structures
3.9
for Loops and if Statements
Chapter 2: Working with Packages, Importing, and Exporting
6
4.1
Installing Packages
4.2
Examining Data
4.3
R Formulas
4.4
More Packages
4.5
Exploring the tidyverse
4.6
Importing and Exporting
Book 2: Describing Data
0
Chapter 1: Getting Graphic
4
6.1
Finding Patterns
6.2
Doing the Basics: Base R Graphics, That Is
6.3
Kicking It Up a Notch to ggplot2
6.4
Putting a Bow On It
Chapter 2: Finding Your Center
7
7.1
Means: The Lure of Averages
7.2
Calculating the Mean
7.3
The Average in R: mean()
7.4
Medians: Caught in the Middle
7.5
The Median in R: median()
7.6
Statistics à la Mode
7.7
The Mode in R
Chapter 3: Deviating from the Average
3
8.1
Measuring Variation
8.2
Back to the Roots: Standard Deviation
8.3
Standard Deviation in R
Chapter 4: Meeting Standards and Standings
4
9.1
Catching Some Zs
9.2
Standard Scores in R
9.3
Where Do You Stand?
9.4
Summarizing
Chapter 5: Summarizing It All
5
10.1
How Many?
10.2
The High and the Low
10.3
Living in the Moments
10.4
Tuning in the Frequency
10.5
Summarizing a Data Frame
Chapter 6: What’s Normal?
3
11.1
Hitting the Curve
11.2
Working with Normal Distributions
11.3
Meeting a Distinguished Member of the Family
Book 3: Analyzing Data
0
Chapter 1: The Confidence Game: Estimation
4
13.1
Understanding Sampling Distributions
13.2
An EXTREMELY Important Idea: The Central Limit Theorem
13.3
Confidence: It Has Its Limits!
13.4
Fit to a t
Chapter 2: One-Sample Hypothesis Testing
11
14.1
Hypotheses, Tests, and Errors
14.2
Hypothesis Tests and Sampling Distributions
14.3
Catching Some Z’s Again
14.4
Z Testing in R
14.5
t for One
14.6
t Testing in R
14.7
Working with t-Distributions
14.8
Visualizing t-Distributions
14.9
Testing a Variance
14.10
Working with Chi-Square Distributions
14.11
Visualizing Chi-Square Distributions
Chapter 3: Two-Sample Hypothesis Testing
10
15.1
Hypotheses Built for Two
15.2
Sampling Distributions Revisited
15.3
t for Two
15.4
Like Peas in a Pod: Equal Variances
15.5
t-Testing in R
15.6
A Matched Set: Hypothesis Testing for Paired Samples
15.7
Paired Sample t-testing in R
15.8
Testing Two Variances
15.9
Working with F Distributions
15.10
Visualizing F Distributions
Chapter 4: Testing More than Two Samples
5
16.1
Testing More than Two
16.2
ANOVA in R
16.3
Another Kind of Hypothesis, Another Kind of Test
16.4
Getting Trendy
16.5
Trend Analysis in R
Chapter 5: More Complicated Testing
5
17.1
Cracking the Combinations
17.2
Two-Way ANOVA in R
17.3
Two Kinds of Variables … at Once
17.4
After the Analysis
17.5
Multivariate Analysis of Variance
Chapter 6: Regression: Linear, Multiple, and the General Linear Model
8
18.1
The Plot of Scatter
18.2
Graphing Lines
18.3
Regression: What a Line!
18.4
Linear Regression in R
18.5
Juggling Many Relationships at Once: Multiple Regression
18.6
ANOVA: Another Look
18.7
Analysis of Covariance: The Final Component of the GLM
18.8
But Wait — There’s More
Chapter 7: Correlation: The Rise and Fall of Relationships
9
19.1
Understanding Correlation
19.2
Correlation and Regression
19.3
Testing Hypotheses about Correlation
19.4
Correlation in R
19.5
Multiple Correlation
19.6
Partial Correlation
19.7
Partial Correlation in R
19.8
Semipartial Correlation
19.9
Semipartial Correlation in R
Chapter 8: Curvilinear Regression: When Relationships Get Complicated
7
20.1
What Is a Logarithm?
20.2
What Is e?
20.3
Power Regression
20.4
Exponential Regression
20.5
Logarithmic Regression
20.6
Polynomial Regression: A Higher Power
20.7
Which Model Should You Use?
Chapter 9: In Due Time
4
21.1
A Time Series and Its Components
21.2
Forecasting: A Moving Experience
21.3
Forecasting: Another Way
21.4
Working with Real Data
Chapter 10: Non-Parametric Statistics
5
22.1
Independent Samples
22.2
Matched Samples
22.3
Correlation: Spearman’s rS
22.4
Correlation: Kendall’s Tau
22.5
A Heads-Up
Chapter 11: Introducing Probability
11
23.1
What Is Probability?
23.2
Compound Events
23.3
Conditional Probability
23.4
Large Sample Spaces
23.5
R Functions for Counting Rules
23.6
Random Variables: Discrete and Continuous
23.7
Probability Distributions and Density Functions
23.8
The Binomial Distribution
23.9
The Binomial and Negative Binomial in R
23.10
Hypothesis Testing with the Binomial Distribution
23.11
More on Hypothesis Testing: R versus Tradition
Chapter 12: Probability Meets Regression: Logistic Regression
3
24.1
Getting the Data
24.2
Doing the Analysis
24.3
Visualizing the Results
Book 4: Learning from Data
0
Chapter 1: Tools and Data for Machine Learning Projects
3
26.1
The UCI (University of California-Irvine) ML Repository
26.2
Introducing the Rattle package
26.3
Using Rattle with iris
Chapter 2: Decisions, Decisions, Decisions
5
27.1
Decision Tree Components
27.2
Decision Trees in R
27.3
Decision Trees in Rattle
27.4
Project: A More Complex Decision Tree
27.5
Suggested Project: Titanic
Chapter 3: Into the Forest, Randomly
4
28.1
Growing a Random Forest
28.2
Random Forests in R
28.3
Project: Identifying Glass
28.4
Suggested Project: Identifying Mushrooms
Chapter 4: Support Your Local Vector
4
29.1
Some Data to Work With
29.2
Separability: It’s Usually Nonlinear
29.3
Support Vector Machines in R
29.4
Project: House Parties
Chapter 5: K-Means Clustering
3
30.1
How It Works
30.2
K-Means Clustering in R
30.3
Project: Glass Clusters
Chapter 6: Neural Networks
5
31.1
Networks in the Nervous System
31.2
Artificial Neural Networks
31.3
Neural Networks in R
31.4
Project: Banknotes
31.5
Suggested Projects: Rattling Around
Chapter 7: Exploring Marketing
3
32.1
Analyzing Retail Data
32.2
Enter Machine Learning
32.3
Suggested Project: Another Data Set
Chapter 8: From the City That Never Sleeps
6
33.1
Examining the Data Set
33.2
Warming Up
33.3
Quick Suggested Project: Airline Names
33.4
Suggested Project: Departure Delays
33.5
Quick Suggested Project: Analyze Weekday Differences
33.6
Suggested Project: Delay and Weather
Book 5: Harnessing R: Some Projects to Keep You Busy
0
Chapter 1: Working with a Browser
5
35.1
Getting Your Shine On
35.2
Creating Your First shiny Project
35.3
Working with ggplot
35.4
Another shiny Project
35.5
Suggested Project
Chapter 2: Dashboards — How Dashing!
4
36.1
The shinydashboard Package
36.2
Exploring Dashboard Layouts
36.3
Working with the Sidebar
36.4
Interacting with Graphics
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