Python and R for the Modern Data Scientist: The Best of Both Worlds

Description

Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. • Learn Python and R from the perspective of your current language • Understand the strengths and weaknesses of each language • Identify use cases where one language is better suited than the other • Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows • Learn how to integrate R and Python in a single workflow • Follow a case study that demonstrates ways to use these languages together Copyright Table of Contents Preface Why We Wrote This Book Technical Interactions Who This Book Is For Prerequisites How This Book Is Organized Let’s Talk Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments Part I. Discovery of a New Language Chapter 1. In the Beginning The Origins of R The Origins of Python The Language War Begins The Battle for Data Science Dominance A Convergence on Cooperation and Community-Building Final Thoughts Part II. Bilingualism I: Learning a New Language Chapter 2. R for Pythonistas Up and Running with R Projects and Packages The Triumph of Tibbles A Word About Types and Exploring Naming (Internal) Things Lists The Facts About Factors How to Find…Stuff Reiterations Redo Final Thoughts Chapter 3. Python for UseRs Versions and Builds Standard Tooling Virtual Environments Installing Packages Notebooks How Does Python, the Language, Compare to R? Import a Dataset Examine the Data Data Structures and Descriptive Statistics Data Structures: Back to the Basics Indexing and Logical Expressions Plotting Inferential Statistics Final Thoughts Part III. Bilingualism II: The Modern Context Chapter 4. Data Format Context External Versus Base Packages Image Data Text Data Time Series Data Base R Prophet Spatial Data Final Thoughts Chapter 5. Workflow Context Defining Workflows Exploratory Data Analysis Static Visualizations Interactive Visualizations Machine Learning Data Engineering Reporting Static Reporting Interactive Reporting Final Thoughts Part IV. Bilingualism III: Becoming Synergistic Chapter 6. Using the Two Languages Synergistically Faux Operability Interoperability Going Deeper Pass Objects Between R and Python in an R Markdown Document Call Python in an R Markdown Document Call Python by Sourcing a Python Script Call Python Using the REPL Call Python with Dynamic Input in an Interactive Document Final Thoughts Chapter 7. A Case Study in Bilingual Data Science 24 Years and 1.88 Million Wildfires Setup and Importing Data EDA and Data Visualization Machine Learning Setting Up Our Python Environment Feature Engineering Model Training Prediction and UI Final Thoughts Appendix A. A Python:R Bilingual Dictionary Package Management Assign Operators Types Arithmetic Operators Attributes Keywords Functions and Methods Style and Naming Conventions Analogous Data Storage Objects Data Frames Logical Expressions Indexing Index About the Authors Colophon