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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

Central tendency

One very popular question to ask about univariate data is What is the typical value? or What's the value around which the data are centered?. To answer these questions, we have to measure the central tendency of a set of data.

We've seen one measure of central tendency already: the mode. The mtcars$carburetors data subset was bimodal, with a two and four carburetor setup being the most popular. The mode is the central tendency measure that is applicable to categorical data.

The mode of a discretized continuous distribution is usually considered to be the interval that contains the highest frequency of data points. This makes it dependent on the method and parameters of the binning. Finding the mode of data from a non-discretized continuous distribution is a more complicated procedure, which we'll see later.

Perhaps the most famous and commonly used measure of central tendency is the mean. The mean is the sum of a set of numerics divided by the number of elements in that set...

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