Data Science with Julia Data Science with Julia By Paul D. McNicholas and Peter A. Tait CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2019 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed on acid-free paper Version Date: 20191119 International Standard Book Number-13: 978-1-138-49998-0 (Paperback) Tis book contains information obtained from authentic and highly regarded sources. Reasonable eforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. Te authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. 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Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifcation and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Names: McNicholas, Paul D., author. | Tait, Peter A., author. Title: Data science with Julia / Paul D. McNicholas, Peter A. Tait. Description: Boca Raton : Taylor & Francis, CRC Press, 2018. | Includes bibliographical references and index. Identifers: LCCN 2018025237 | ISBN 9781138499980 (pbk.) Subjects: LCSH: Julia (Computer program language) | Data structures (Computer science) Classifcation: LCC QA76.73.J85 M37 2018 | DDC 005.7/3--dc23 LC record available at https://lccn.loc.gov/2018025237 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com For Oscar, who tries and tries. PDM To my son, Xavier, Gettin’ after it does pay off. PAT Contents Chapter 1 ■ Introduction 1 1.1 DATA SCIENCE 1 1.2 BIG DATA 4 1.3 JULIA 5 1.4 JULIA AND R PACKAGES 6 1.5 DATASETS 6 1.5.1 Overview 6 1.5.2 Beer Data 6 1.5.3 Coffee Data 7 1.5.4 Leptograpsus Crabs Data 8 1.5.5 Food Preferences Data 9 1.5.6 x2 Data 9 1.5.7 Iris Data 11 1.6 OUTLINE OF THE CONTENTS OF THIS MONOGRAPH 11 Chapter 2 ■ Core Julia 13 2.1 VARIABLE NAMES 13 2.2 OPERATORS 14 2.3 TYPES 15 2.3.1 Numeric 15 2.3.2 Floats 17 2.3.3 Strings 19 2.3.4 Tuples 22 2.4 DATA STRUCTURES 23 2.4.1 Arrays 23 vii viii ■ Contents 2.4.2 Dictionaries 26 2.5 CONTROL FLOW 28 2.5.1 Compound Expressions 28 2.5.2 Conditional Evaluation 29 2.5.3 Loops 30 2.5.3.1 Basics 30 2.5.3.2 Loop termination 32 2.5.3.3 Exception handling 33 2.6 FUNCTIONS 36 Chapter 3 ■ Working with Data 43 3.1 DATAFRAMES 43 3.2 CATEGORICAL DATA 47 3.3 INPUT/OUTPUT 48 3.4 USEFUL DATAFRAME FUNCTIONS 54 3.5 SPLIT-APPLY-COMBINE STRATEGY 56 3.6 QUERY.JL 59 Chapter 4 ■ Visualizing Data 67 4.1 GADFLY.JL 67 4.2 VISUALIZING UNIVARIATE DATA 69 4.3 DISTRIBUTIONS 72 4.4 VISUALIZING BIVARIATE DATA 83 4.5 ERROR BARS 90 4.6 FACETS 91 4.7 SAVING PLOTS 91 Chapter 5 ■ Supervised Learning 93 5.1 INTRODUCTION 93 5.2 CROSS-VALIDATION 96 5.2.1 Overview 96 5.2.2 K-Fold Cross-Validation 97 5.3 K-NEAREST NEIGHBOURS CLASSIFICATION 99 5.4 CLASSIFICATION AND REGRESSION TREES 102 Contents ■ ix 5.4.1 Overview 102 5.4.2 Classification Trees 103 5.4.3 Regression Trees 106 5.4.4 Comments 108 5.5 BOOTSTRAP 108 5.6 RANDOM FORESTS 111 5.7 GRADIENT BOOSTING 113 5.7.1 Overview 113 5.7.2 Beer Data 116 5.7.3 Food Data 121 5.8 COMMENTS 126 Chapter 6 ■ Unsupervised Learning 129 6.1 INTRODUCTION 129 6.2 PRINCIPAL COMPONENTS ANALYSIS 132 6.3 PROBABILISTIC PRINCIPAL COMPONENTS ANALYSIS 135 6.4 EM ALGORITHM FOR PPCA 137 6.4.1 Background: EM Algorithm 137 6.4.2 E-step 138 6.4.3 M-step 139 6.4.4 Woodbury Identity 140 6.4.5 Initialization 141 6.4.6 Stopping Rule 141 6.4.7 Implementing the EM Algorithm for PPCA 142 6.4.8 Comments 146 6.5 K-MEANS CLUSTERING 148 6.6 MIXTURE OF PROBABILISTIC PRINCIPAL COM- PONENTS ANALYZERS 151 6.6.1 Model 151 6.6.2 Parameter Estimation 152 6.6.3 Illustrative Example: Coffee Data 161 6.7 COMMENTS 162