Hands-On Data Science for Librarians
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Cover Half Title Title Page Copyright Page Dedication Contents List of Figures List of Table Preface About the Authors 1. Introduction 1.1. What Is Data Science? 1.2. Why Learn Data Science? 1.3. Why Use Code? 1.4. Vignette 1.5. Structure of This Book 1.6. Who This Book Is for 2. Using RStudio’s IDE 2.1. Learning Objectives 2.2. Terms You’ll Learn 2.3. Scenario 2.4. Introduction 2.5. What is R? 2.6. Introducing the Tidyverse 2.7. Getting Started with the RStudio IDE 2.8. Packages Needed for this Book 2.9. Viewing Tabular Data in RStudio 2.10. Summary 2.11. Further Practice 2.12. Additional Resources 3. Tidying Data with dplyr 3.1. Learning Objectives 3.2. Terms You’ll Learn 3.3. Scenario 3.4. Packages & Datasets Needed 3.5. Introduction 3.6. Getting Started with U.S. Census Data 3.7. Tidy Data Tools from dplyr 3.8. Getting Started with dplyr Functions 3.9. Occupation Data 3.10. Clean Up Metadata 3.11. Create a CSV of Unemployment Data 3.12. Summary 3.13. Further Practice 3.14. Additional Resources 4. Visualizing Your Project with ggplot2 4.1. Learning Objectives 4.2. Terms You’ll Learn 4.3. Scenario 4.4. Packages & Datasets Needed 4.5. Introduction to ggplot2 4.6. Components of a plot 4.7. Coordinate Systems 4.8. Unemployment plots 4.9. Additional ggplot2 Concepts 4.10. Summary 4.11. Additional Resources 4.12. Further Practice 5. Webscraping with rvest 5.1. Learning Objectives 5.2. Terms You’ll Learn 5.3. Scenario 5.4. Packages & Datasets Needed 5.5. Introduction 5.6. Identifying & Scraping Website Components 5.7. Web Scraping Part 1: Wards & Aldermen 5.8. Web Scraping Part 2: Email Addresses on Individual Pages 5.9. Create Reusable Data File 5.10. Summary 5.11. Further Practice 5.12. Additional Resources 6. Mapping with tmap 6.1. Learning Objectives 6.2. Terms You’ll Learn 6.3. Scenario 6.4. Packages & Datasets Needed 6.5. An Overview of Spatial Data 6.6. Loading in the Data 6.7. Summary 6.8. Further Practice 6.9. Additional Resources 7. Textual Analysis with tidytext 7.1. Learning Objectives 7.2. Terms You’ll Learn 7.3. Scenario 7.4. Packages & Datasets Needed 7.5. Introduction 7.6. Query the NYT Article Database 7.7. Tokenization 7.8. Stop Words 7.9. Sentiment Analysis 7.10. TF-IDF 7.11. Summary 7.12. Further Practice 7.13. Additional Resources 8. Creating Dynamic Documents with rmarkdown 8.1. Learning Objectives 8.2. Scenario 8.3. Introduction to R Markdown 8.4. Packages & Datasets Needed 8.5. Creating an R Markdown Document 8.6. R Markdown Document Structure 8.7. Adding Data Visualizations to Your Markdown Document 8.8. Creating Your Output 8.9. Customizing Your R Markdown Document with Templates 8.10. Summary 8.11. Resources 8.12. Further Practice 9. Creating a Flexdashboard 9.1. Learning Objectives 9.2. Terms You’ll Learn 9.3. Scenario 9.4. Overview of flexdashboard 9.5. Packages and Datasets Needed 9.6. Initiating the flexdashboard 9.7. Creating the Pages 9.8. Creating the Columns 9.9. Adding the Code Chunks 9.10. Summary 9.11. Further Practice 9.12. Resources 10. Creating an Interactive Dashboard with Shiny 10.1. Learning Objectives 10.2. Terms You’ll Learn 10.3. Scenario 10.4. Packages and datasets needed 10.5. What You Need to Know About Shiny 10.6. Creating a Shiny Web Map App 10.7. Creating a ggplot Shiny App 10.8. Integrating Shiny Apps into a Flexdashboard 10.9. Summary 10.10. Further Practice 10.11. Resources 11. Using tidymodels to Understand Machine Learning 11.1. Learning Objectives 11.2. Terms You’ll Learn 11.3. Scenario 11.4. Packages & Datasets Needed 11.5. Introduction 11.6. What ML is 11.7. How ML Works in R (tidymodels) 11.8. Problems with Machine Learning 11.9. Machine Learning in Employment 11.10. Summary 11.11. Further Practice 11.12. Additional Resources 12. Conclusion 12.1. Wrapping Up...for Now 12.2. Where Do You Go from Here? 12.3. Data Management: Never Do Work Without It 12.4. Final Send-off on Your Data Science Journey A. Dependencies A.1. iOS Dependencies A.2. Windows Dependencies A.3. Package Dependencies for This Book B. Additional Skills B.1. Using the Shell or Command Line on Your Computer B.2. Using GitHub & Git B.3. Troubleshooting Package Installation Problems Bibliography Index
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