The Big R-Book: From Data Science to Learning Machines and Big Data
by Philippe J.S. De Brouwer
– Published by John Wiley & Sons –

Contents of This Page

  1. The Parts of the Book
  2. Some Statistics about the Book
  3. Downloads, Sources, and Sample Material
  4. Sample Applications that You Learn to Build in the Book
  5. Get Your Copy
  6. Citations

The Parts of the Book

  1. Introduction: the importance of data science
  2. Starting with R and Elements of Statistics: the basics
  3. Data Import: SQL databases and importing data in R
  4. Data Wrangling: modifying data
  5. Modelling: building robust models and verify them, including popular machine learning techniques
  6. Introduction to Companies: what are companies, financial and management accounting, valuation of financial assets and multi criteria decision analysis
  7. Reporting: from automated slides to interactive websites
  8. Bigger and Faster R: using clusters, programming the GPU, big data, and code optimisation
  9. Appendices: extra code, notes, and levels of measurement

Some Statistics about the Book

  • 803 R code examples or segments and 92 other code sections
  • 145 illustrations
  • 270 boxes with information, warnings, digressions, and further information
  • 9 parts, front and back-matter
  • 55 chapters, 129 sections, 223 subsections, 128 subsubsections
  • 1,100+ pages
  • 258,450 words in 34,438 paragraphs

Downloads, Source Code, Sample Material, and Extra Resources

Sample Material

Samples (note: these samples are generate from the submission to the editor – so, while the words are the same, it might look different in the book itself):

Further Examples

Examples of dynamic websites with R:

Source Code, Slides and Videos Per Chapter

A series of videos based on the book: YouTube Playlist
# Title R-Code Slides Videos
1 Introduction p01.R Ch 1: Introduction: The past and the future of science
2 Starting with R and Elements of Statistics p02.R Ch 4: How to install R on Windows / the big R-Book series / ch.4
Ch 4: Getting Started with R (console version)
Ch 4: Getting Started with R (RStudio Version)
Ch 7: The philosophy of the Tidyverse
Ch 7: Getting Started the Tidyverse
3 Data Import p03.R
4 Data Wrangling p04.R
5 Modelling p05.R
6 Introduction to Companies p06.R
7 Reporting p07.R Ch 36: Interactive Dashboards (example with Covid19)
8 Bigger and Faster R p08.R
9 Appendices p09.R

Sample Applications that You Learn to Build in the Book

This book is about many things, such as databaes, big data, models, model validation, etc. Most of things are better seen and read in the book. Dynamic dashboards and interactive websites, however, are best experienced here on a website. Therefore, we offer you here some of the applications that the book teaches you to make.

The Static Diversity Dashboard

This example is a diversity dashboard for a company or team. The data from the HR team is presented from different angles. Don't forget to select the other tabs in the blue menu bar, view the code with the button on the right, … and even the button to share the dashboard on social media works. However, abouve all it is interesting to see the interaction that is possible with the plots. For example, click [gender], [salary], and then move the cursor over the plot to get the menu bar. You can now zoom in, see values, etc.

Alternatively, you can find the dashboard here: on rpubs

Simulation of the Normal Distribution

This simple example takes random numbers from the normal distribution and then plot the historgram. This is useful to experiment and get a feeling what sample size can do and how the binning of the historgram influences your perception of the distribution.

This is dicussed in the book in section 36.1, Shiny.

Get Your Copy

Available now on your favourite shop. For example:

Citations

Automated citations for each chapter: onlinelibrary.wiley.com.

Here is also the bibtex code for the print-version:
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@book{debrouwer2020,
  title     = {The Big R-Book: From Data Science to Learning Machines and Big Data},
  author    = {Philippe J.S. {De Brouwer}},
  year      = {2020},
  publisher = {John Wiley & Sons, Ltd},
  address   = {New York},
  isbn      = {978-1-119-63272-6}
}
and for the online version:
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@book{debrouwer2020online,
  title     = {The Big R-Book: From Data Science to Learning Machines and Big Data},
  author    = {Philippe J.S. {De Brouwer}},
  year      = {2020},
  publisher = {John Wiley & Sons, Ltd},
  address   = {New York},
  isbn      = {978-1-119-63275-7},
  doi       = {https://doi.org/10.1002/9781119632757},
  url       = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119632757},
  eprint    = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119632757}
}