Doing Data Science in R
eBook - ePub

Doing Data Science in R

An Introduction for Social Scientists

  1. 456 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Doing Data Science in R

An Introduction for Social Scientists

About this book

This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. 

This book:

  • Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires
  • Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills
  • Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software
  • Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.

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Yes, you can access Doing Data Science in R by Mark Andrews in PDF and/or ePUB format, as well as other popular books in Social Sciences & Research & Methodology in Psychology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Acknowledgements
  4. Title Page
  5. Copyright Page
  6. Contents
  7. About the Author
  8. Online Resources
  9. 1 Data Analysis and Data Science
  10. Part I Fundamentals of Data Analysis and Data Science
  11. 2 Introduction to R
  12. 3 Data Wrangling
  13. 4 Data Visualization
  14. 5 Exploratory Data Analysis
  15. 6 Programming in R
  16. 7 Reproducible Data Analysis
  17. Part II Statistical Modelling
  18. 8 Statistical Models and Statistical Inference
  19. 9 Normal Linear Models
  20. 10 Logistic Regression
  21. 11 Generalized Linear Models for Count Data
  22. 12 Multilevel Models
  23. 13 Nonlinear Regression
  24. 14 Structural Equation Modelling
  25. Part III Advanced or Special Topics in Data Analysis
  26. 15 High-Performance Computing with R
  27. 16 Interactive Web Apps with Shiny
  28. 17 Probabilistic Modelling with Stan
  29. References
  30. Index