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Apress

Beginning Data Science in R 4

Data Analysis, Visualization, and Modelling for the Data Scientist

  • Book
  • © 2022

Overview

Authors:
  1. Thomas Mailund

    1. Aarhus, Denmark

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  • Gives you everything you need to know to get started in data science using R language
  • Updated for R programming language version 4.0
  • A unique book by a data science expert and is based on a successful lecture series

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xxviii
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  2. Introduction to R Programming

    • Thomas Mailund
    Pages 1-50

  3. Reproducible Analysis

    • Thomas Mailund
    Pages 51-72

  4. Data Manipulation

    • Thomas Mailund
    Pages 73-119

  5. Visualizing Data

    • Thomas Mailund
    Pages 121-160

  6. Working with Large Data Sets

    • Thomas Mailund
    Pages 161-177

  7. Supervised Learning

    • Thomas Mailund
    Pages 179-238

  8. Unsupervised Learning

    • Thomas Mailund
    Pages 239-274

  9. Project 1: Hitting the Bottle

    • Thomas Mailund
    Pages 275-285

  10. Deeper into R Programming

    • Thomas Mailund
    Pages 287-328

  11. Working with Vectors and Lists

    • Thomas Mailund
    Pages 329-348

  12. Functional Programming

    • Thomas Mailund
    Pages 349-372

  13. Object-Oriented Programming

    • Thomas Mailund
    Pages 373-389

  14. Building an R Package

    • Thomas Mailund
    Pages 391-407

  15. Testing and Package Checking

    • Thomas Mailund
    Pages 409-417

  16. Version Control

    • Thomas Mailund
    Pages 419-440

  17. Profiling and Optimizing

    • Thomas Mailund
    Pages 441-469

  18. Project 2: Bayesian Linear Regression

    • Thomas Mailund
    Pages 471-500

  19. Back Matter

    Pages 501-511
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Keywords

About this book

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. 


Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. 


Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications.  Source code will be available to support your next projects as well.

Source code is available at github.com/Apress/beg-data-science-r4.




What You Will Learn
  • Perform data science and analytics using statistics and the R programming language
  • Visualize and explore data, including working with large data sets found in big data
  • Build an R package
  • Test and check your code
  • Practice version control
  • Profile and optimize your code



Who This Book Is For


Those with some data science or analytics background, but not necessarily experience with the R programming language.


Authors and Affiliations

  • Aarhus, Denmark

    Thomas Mailund

About the author

Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science but for the last decade his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.


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