Data analysis with R : a comprehensive uide to manipulating, analyzing, and visualizing data in R / Anthony Fischetti.

Format:

Book

Author/Creator:

Fischetti, Anthony, author.

Language:

English

Subjects (All):

Information visualization.

Database design.

Physical Description:

1 online resource (570 pages)

Edition:

Second edition.

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2018.

Summary:

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. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 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

Chapter 2: The Shape of Data

Univariate data

Frequency distributions

Central tendency

Spread

Populations, samples, and estimation

Probability distributions

Visualization methods

Chapter 3: Describing Relationships

Multivariate data

Relationships between a categorical and continuous variable

Relationships between two categorical variables

The relationship between two continuous variables

Covariance

Correlation coefficients

Comparing multiple correlations

Categorical and continuous variables

Two categorical variables

Two continuous variables

More than two continuous variables

Chapter 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

Chapter 5: Using Data To Reason About The World

Estimating means

The sampling distribution

Interval estimation

How did we get 1.96?

Smaller samples

Chapter 6: Testing Hypotheses

The null hypothesis significance testing framework

One and two-tailed tests

Errors in NHST

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.

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?

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

Chapter 8: The Bootstrap

What's... uhhh... the deal with the bootstrap?

Performing the bootstrap in R (more elegantly)

Confidence intervals

A one-sample test of means

Bootstrapping statistics other than the mean

Busting bootstrap myths

What have we left out?

Chapter 9: 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

Chapter 10: Predicting Categorical Variables

k-Nearest neighbors

Using k-NN in R

Confusion matrices

Limitations of k-NN

Logistic regression

Generalized Linear Model (GLM)

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

Chapter 11: Predicting Changes with Time

What is a time series?

What is forecasting?

Uncertainty

Difficulties in forecasting.

Creating and plotting time series

Components of time series

Time series decomposition

White noise

Autocorrelation

Smoothing

Simple exponential smoothing for forecasting

Accuracy assessment

Double exponential smoothing

Triple exponential smoothing

ETS and the state space model

Interventions for improvement

What we didn't cover

Citations for the climate change data

Chapter 12: Sources of Data

Relational databases

Why didn't we just do that in SQL?

Using JSON

XML

Other data formats

Online repositories

Chapter 13: Dealing with Missing 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

Chapter 14: Dealing with Messy Data

Checking 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

Regular expressions

What are regular expressions?

Getting started

Regex for data normalization

More normalization

Other tools for messy data

OpenRefine

Fuzzy matching

Chapter 15: 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

Using parallelization.

Getting started with parallel R

An example of (some) substance

Using Rcpp

Being smarter about your code

Chapter 16: Working with Popular R Packages

The data.table package

The i in DT [i, j, by]

What in the world are by reference semantics?

The j in DT[i, j, by]

Using both i and j

Using the by argument for grouping

Joining data tables

Reshaping, melting, and pivoting data

Using dplyr and tidyr to manipulate data

Functional programming as a main tidyverse principle

Loading data for use in dplyr

Manipulating rows

Selecting and renaming columns

Computing on columns

Grouping in dplyr

Joining data

Reshaping data with tidyr

Chapter 17: Reproducibility and Best Practices

R scripting

RStudio

Running R scripts

An example script

Scripting and reproducibility

R projects

Version control

Package version management

Communicating results

Other Books You May Enjoy

Index.

Notes:

Description based on print version record.

ISBN:

9781788397339

1788397339

OCLC:

1030818399