Data Analysis with R电子书

Data Analysis with R

Table of Contents

Data Analysis with R

Credits

About the Author

About the Reviewer

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. RefresheR

Navigating the basics

Arithmetic and assignment

Logicals and characters

Flow of control

Getting help in R

Vectors

Subsetting

Vectorized functions

Advanced subsetting

Recycling

Functions

Matrices

Loading data into R

Working with packages

Exercises

Summary

2. The Shape of Data

Univariate data

Frequency distributions

Central tendency

Spread

Populations, samples, and estimation

Probability distributions

Visualization methods

Exercises

Summary

3. Describing Relationships

Multivariate data

Relationships between a categorical and a continuous variable

Relationships between two categorical variables

The relationship between two continuous variables

Covariance

Correlation coefficients

Comparing multiple correlations

Visualization methods

Categorical and continuous variables

Two categorical variables

Two continuous variables

More than two continuous variables

Exercises

Summary

4. Probability

Basic probability

A tale of two interpretations

Sampling from distributions

Parameters

The binomial distribution

The normal distribution

The three-sigma rule and using z-tables

Exercises

Summary

5. Using Data to Reason About the World

Estimating means

The sampling distribution

Interval estimation

How did we get 1.96?

Smaller samples

Exercises

Summary

6. Testing Hypotheses

Null Hypothesis Significance Testing

One and two-tailed tests

When things go wrong

A warning about significance

A warning about p-values

Testing the mean of one sample

Assumptions of the one sample t-test

Testing two means

Don't be fooled!

Assumptions of the independent samples t-test

Testing more than two means

Assumptions of ANOVA

Testing independence of proportions

What if my assumptions are unfounded?

Exercises

Summary

7. Bayesian Methods

The big idea behind Bayesian analysis

Choosing a prior

Who cares about coin flips

Enter MCMC – stage left

Using JAGS and runjags

Fitting distributions the Bayesian way

The Bayesian independent samples t-test

Exercises

Summary

8. Predicting Continuous Variables

Linear models

Simple linear regression

Simple linear regression with a binary predictor

A word of warning

Multiple regression

Regression with a non-binary predictor

Kitchen sink regression

The bias-variance trade-off

Cross-validation

Striking a balance

Linear regression diagnostics

Second Anscombe relationship

Third Anscombe relationship

Fourth Anscombe relationship

Advanced topics

Exercises

Summary

9. Predicting Categorical Variables

k-Nearest Neighbors

Using k-NN in R

Confusion matrices

Limitations of k-NN

Logistic regression

Using logistic regression in R

Decision trees

Random forests

Choosing a classifier

The vertical decision boundary

The diagonal decision boundary

The crescent decision boundary

The circular decision boundary

Exercises

Summary

10. Sources of Data

Relational Databases

Why didn't we just do that in SQL?

Using JSON

XML

Other data formats

Online repositories

Exercises

Summary

11. Dealing with Messy Data

Analysis with missing data

Visualizing missing data

Types of missing data

So which one is it?

Unsophisticated methods for dealing with missing data

Complete case analysis

Pairwise deletion

Mean substitution

Hot deck imputation

Regression imputation

Stochastic regression imputation

Multiple imputation

So how does mice come up with the imputed values?

Methods of imputation

Multiple imputation in practice

Analysis with unsanitized data

Checking for out-of-bounds data

Checking the data type of a column

Checking for unexpected categories

Checking for outliers, entry errors, or unlikely data points

Chaining assertions

Other messiness

OpenRefine

Regular expressions

tidyr

Exercises

Summary

12. Dealing with Large Data

Wait to optimize

Using a bigger and faster machine

Be smart about your code

Allocation of memory

Vectorization

Using optimized packages

Using another R implementation

Use parallelization

Getting started with parallel R

An example of (some) substance

Using Rcpp

Be smarter about your code

Exercises

Summary

13. Reproducibility and Best Practices

R Scripting

RStudio

Running R scripts

An example script

Scripting and reproducibility

R projects

Version control

Communicating results

Exercises

Summary

Index