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A Minimal Book Example
About
1
Introduction to R
1.1
What We Talk About When We Talk About R
1.1.1
Jupyter Notebooks & Google Colab
1.1.2
RStudio
1.2
R Packages
1.2.1
Tidyverse
1.3
Reading This Book
1.3.1
Code Examples
1.3.2
Function Syntax
1.3.3
Warnings
2
The Basics of R
2.1
The Building Blocks of Data
2.2
Data Types
2.3
Data Structures
2.3.1
Atomic Vectors
2.3.2
Matrices
2.3.3
Lists
2.3.4
Data Frames
3
Wrangling Data
3.1
A Brief Note on Packages
3.2
Reading in Data
3.3
Data Frame Basics
3.4
Fixing Variable Types
3.4.1
Fixing Numeric Variables
3.4.2
Fixing Factor Variables
3.4.3
Fixing Date Variables
3.5
Sorting Data
3.6
Filtering Rows
3.7
Selecting Columns
3.8
Creating New Columns
3.8.1
Helper Functions
3.9
Combining Steps with the Pipe
3.10
Joining Data
4
Exploring and Summarizing Data
4.1
Summary Statistics for Quantitative Variables
4.2
Summary Statistics for Categorical Variables
4.3
Exploratory Visualizations
4.3.1
Histograms
4.3.2
Boxplots
4.3.3
Side-by-Side Box Plots
4.3.4
Scatter Plots
4.3.5
Bar Plots
5
Programming Concepts
5.1
Conditional Statements
5.1.1
if/else
Statements
5.1.2
for
Loops
5.1.3
while
Loops
5.2
Functions
6
Statistical Inference
6.1
Confidence Intervals
6.2
Hypothesis Testing
6.2.1
Formulating Hypotheses
6.2.2
The Logic of Hypothesis Testing
6.2.3
The P-Value
6.2.4
Some P-Value Cautions
6.2.5
One-Sample Hypothesis Testing
6.2.6
Two-Sample Hypothesis Testing
6.2.7
Hypothesis Testing with More Than Two Samples
7
Regression Modeling
7.1
Linear Regression
7.1.1
Correlation
7.1.2
Simple Linear Regression
7.1.3
Multiple Linear Regression
7.1.4
Dummy Variables
7.1.5
Transformations
7.1.6
Interactions
7.1.7
Diagnostics
7.1.8
Examples
7.2
Logistic Regression
8
Supervised Machine Learning with
caret
8.1
The Bias-Variance Tradeoff
8.1.1
Train-Test-Holdout
8.1.2
k
-Fold Cross Validation
8.2
Regression Modeling (Revised)
8.2.1
Regularization
8.3
CART Models
8.3.1
Decision Trees
8.3.2
Regression Trees
8.4
Random Forest Models
8.5
Neural Networks
References
Published with bookdown
Practical Data Skills
Chapter 4
Exploring and Summarizing Data
4.1
Summary Statistics for Quantitative Variables
4.2
Summary Statistics for Categorical Variables
4.3
Exploratory Visualizations
4.3.1
Histograms
4.3.2
Boxplots
4.3.3
Side-by-Side Box Plots
4.3.4
Scatter Plots
4.3.5
Bar Plots