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Book 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. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you'll discover new ways to accomplish data science tasks and expand your skill set.

Authors Boyan Angelov and Rick Scavetta 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. Not only will you learn how to use Python and R together in real-world settings, but you'll also broaden your knowledge and job opportunities by working 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 real-world case study that demonstrates ways to use these languages together

Table of Contents

  1. 1. R for Pythonistas
    1. Up and running with R
      1. RStudio Cloud
      2. RStudio Desktop
    2. The perks & perils of projects & packages
      1. Installing and loading packages
    3. The Triumph of Tibbles
    4. A word about types and exploring
      1. Vectors
      2. Vector types have an inherent Hierarchy
    5. Naming (internal) Things
    6. List the ways
    7. The Facts about Factors
    8. How to find… stuff
    9. Reiterations Redo
    10. Final Thoughts
  2. 2. Data Format Context
    1. Definitions and landscape
    2. Packages versus built-in
    3. Tabular data
    4. Image data
      1. OpenCV and scikit-image
    5. Text data
      1. NLTK and spaCy
    6. Time series data
      1. Base R
      2. prophet
    7. Spatial data
      1. raster
    8. Bringing it all together
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