Book Image

Data Analysis with R

Book Image

Data Analysis with R

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it’s easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.

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Data Analysis with R
Dec, 2015 | 388 pages
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Table of Contents (20 chapters)
Data Analysis with R
Data Analysis with R
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
RefresheR
RefresheR
Navigating the basics
Getting help in R
Vectors
Functions
Matrices
Loading data into R
Working with packages
Exercises
Summary
2
The Shape of Data
The Shape of Data
Univariate data
Frequency distributions
Central tendency
Spread
Populations, samples, and estimation
Probability distributions
Visualization methods
Exercises
Summary
3
Describing Relationships
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
4
Probability
Probability
Basic probability
A tale of two interpretations
Sampling from distributions
The normal distribution
Exercises
Summary
5
Using Data to Reason About the World
Using Data to Reason About the World
Estimating means
The sampling distribution
Interval estimation
Smaller samples
Exercises
Summary
6
Testing Hypotheses
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
7
Bayesian Methods
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
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
9
Predicting Categorical Variables
Predicting Categorical Variables
k-Nearest Neighbors
Logistic regression
Decision trees
Random forests
Choosing a classifier
Exercises
Summary
10
Sources of Data
Sources of Data
Relational Databases
Using JSON
XML
Other data formats
Online repositories
Exercises
Summary
11
Dealing with Messy Data
Dealing with Messy Data
Analysis with missing data
Analysis with unsanitized data
Other messiness
Exercises
Summary
12
Dealing with Large Data
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
13
Reproducibility and Best Practices
Reproducibility and Best Practices
R Scripting
R projects
Version control
Communicating results
Exercises
Summary
Index
Index

Wait to optimize

Prominent computer scientist and mathematician Donald Knuth famously stated:

Premature optimization is the root of all evil.

I, personally, hold that money is the root of all evil, but premature optimization is definitely up there!

Why is premature optimization so evil? Well, there are a few reasons. First, programmers can sometimes be pretty bad at identifying what the bottleneck of a program—the routine(s) that have the slowest throughput—is and optimize the wrong parts of a program. Identification of bottlenecks can most accurately be performed by profiling your code after it's been completed in an un-optimized form.

Secondly, clever tricks and shortcuts for speeding up code often introduce subtle bugs and unexpected behavior. Now, the speedup of the code—if there is any!—must be taken in context with the time it took to complete the bug-finding-and-fixing expedition; occasionally, a net negative amount of time has been saved when all is said and done.

Lastly, since premature...

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