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Data Analysis with R

更新時間：2021-07-30 09:55:45

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Whetheryouarelearningdataanalysisforthefirsttime,oryouwanttodeepentheunderstandingyoualreadyhave,thisbookwillprovetoaninvaluableresource.Ifyouarelookingforabooktobringyouallthewaythroughthefundamentalstotheapplicationofadvancedandeffectiveanalyticsmethodologies,andhavesomepriorprogrammingexperienceandamathematicalbackground,thenthisisforyou.

目錄(128章)

倒序

封面
版權頁
Credits
About the Author
About the Reviewer
www.PacktPub.com
Support files eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. RefresheR
Navigating the basics
Getting help in R
Vectors
Functions
Matrices
Loading data into R
Working with packages
Exercises
Summary
Chapter 2. The Shape of Data
Univariate data
Frequency distributions
Central tendency
Spread
Populations samples and estimation
Probability distributions
Visualization methods
Exercises
Summary
Chapter 3. Describing Relationships
Multivariate data
Relationships between a categorical and a continuous variable
Relationships between two categorical variables
The relationship between two continuous variables
Visualization methods
Exercises
Summary
Chapter 4. Probability
Basic probability
A tale of two interpretations
Sampling from distributions
The normal distribution
Exercises
Summary
Chapter 5. Using Data to Reason About the World
Estimating means
The sampling distribution
Interval estimation
Smaller samples
Exercises
Summary
Chapter 6. Testing Hypotheses
Null Hypothesis Significance Testing
Testing the mean of one sample
Testing two means
Testing more than two means
Testing independence of proportions
What if my assumptions are unfounded?
Exercises
Summary
Chapter 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
Chapter 8. Predicting Continuous Variables
Linear models
Simple linear regression
Simple linear regression with a binary predictor
Multiple regression
Regression with a non-binary predictor
Kitchen sink regression
The bias-variance trade-off
Linear regression diagnostics
Advanced topics
Exercises
Summary
Chapter 9. Predicting Categorical Variables
k-Nearest Neighbors
Logistic regression
Decision trees
Random forests
Choosing a classifier
Exercises
Summary
Chapter 10. Sources of Data
Relational Databases
Using JSON
XML
Other data formats
Online repositories
Exercises
Summary
Chapter 11. Dealing with Messy Data
Analysis with missing data
Analysis with unsanitized data
Other messiness
Exercises
Summary
Chapter 12. Dealing with Large Data
Wait to optimize
Using a bigger and faster machine
Be smart about your code
Using optimized packages
Using another R implementation
Use parallelization
Using Rcpp
Be smarter about your code
Exercises
Summary
Chapter 13. Reproducibility and Best Practices
R Scripting
R projects
Version control
Communicating results
Exercises
Summary
Index 更新時間：2021-07-30 09:55:45

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Data Analysis with R